Face detection and tracking is becoming an increasingly more important technology. In this regard, for example, face detection may be useful in biometrics, user interface, gaming and other areas such as creating context for accessing communities in the mobile domain. Advancement in computing power of microprocessors has also made facial analysis functionality available on mobile devices, such as cell phones and other smart devices. Facial analysis may also be important going forward in relation to initiatives such as metadata standardization.
Although face detection techniques continue to improve, many current methods require either a high computation capability (e.g., statistical methods of detecting faces by scanning images in a traversing way on multiple scales) or suffer from limited face detection performance (e.g., structure-based methods with relatively high false alarms of face detection). Furthermore, some statistical face detection mechanisms have degraded performance for multi-view face detection in relation to front face detection. As another complicating issue, faces encountered in a particular image may not always be oriented in the same direction relative to the camera, which could negatively impact face detection. For example, in-plane rotation (e.g., faces that are rotated along the axis from the face to the observer) may complicate face detection in some cases.
Accordingly, the tendency for developing devices with continued increases in their capacity to create content, store content and/or receive content relatively quickly upon request, the trend toward electronic devices (e.g., mobile electronic devices such as mobile phones) becoming increasingly ubiquitous in the modern world, and the drive for continued improvements in interface and access mechanisms to unlock the capabilities of such devices, may make it desirable to provide further improvements in the area of face detection.